Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

TL;DR

This paper presents a scalable, privacy-preserving algorithm for generating accurate location heatmaps from decentralized user data, balancing differential privacy, resource efficiency, and high accuracy.

Contribution

It introduces a novel scalable distributed differential privacy method using secure multiparty computation for location analytics, improving efficiency and privacy guarantees.

Findings

Successfully generates metropolitan-scale heatmaps from millions of users

Achieves lower client communication overhead than existing protocols

Maintains high data accuracy under differential privacy constraints

Abstract

We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high data accuracy and minimizing resource consumption on users' devices. To achieve this, we revisit distributed differential privacy based on recent results in secure multiparty computation, and we design a scalable and adaptive distributed differential privacy approach for location analytics. Evaluation on public location datasets shows that this approach successfully generates metropolitan-scale heatmaps from millions of user samples with a worst-case client communication overhead that is significantly smaller than existing state-of-the-art private protocols of similar accuracy.